Complex Internet Data Management System

Zosimov, Viacheslav; Bulgakova, Oleksandra; Pozdeev, V. A.

doi:10.1007/978-3-030-54215-3_41

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Bayes' theorem is used if there is information on the dependent variables in the given order, and the essence of the study is to determine the probability of the initial variables [5]. So, let the conditional probability P(B|A) of the occurrence of some event B be known under the condition that event A occurs.…”

Section: Mathematical Model Of the Search Results Ranking Systemmentioning

confidence: 99%

Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

Zosimov¹

2023

Kibern. vyčisl. teh.

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Introduction. This paper proposes a probabilistic approach to ranking search results using Bayesian Belief Networks (BBN). The proposed approach utilizes BBN to model the relationships between search queries, web pages, and user feedback, and to calculate the probability of a web page being relevant to a specific query. The approach takes into account various factors, such as keywords, page relevance, domain authority, and user feedback to generate a ranking score for each search result.The purpose of the paper is to conduct an analysis on the feasibility of creating a search engine that uses BBNs and probabilistic ranking methods for improving the accuracy and efficiency of search results.Results. The proposed approach was evaluated on a real-world dataset, and the results showed its effectiveness. Overall, the results suggest that the use of BBNs can provide a promising approach to enhancing search engine performance and user experience. The approach's effectiveness is attributed to its ability to model and reason about uncertainty and dependencies among variables, and its consideration of various factors, such as keywords, page relevance, domain authority, and user feedback. Conclusions.The proposed method has the potential to improve search relevance, reduce user frustration, and increase user satisfaction. However, further research is needed to optimize the proposed approach and to explore its applicability in different contexts. Overall, the study suggests that BBNs can provide a valuable tool for developing more effective and user-friendly search engines. Moreover, the use of Sphinx as a base search system shows promise in enabling the proposed approach to be integrated into practical search systems. Nonetheless, further research is needed to optimize the approach and evaluate its applicability in different contexts.

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Section: Mathematical Model Of the Search Results Ranking Systemmentioning

confidence: 99%

Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

Zosimov¹

2023

Kibern. vyčisl. teh.

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“…In [10], a learning-based ranking model is proposed to enhance recommendation systems using implicit user feedback. Adaptive learning is described in [11] to improve content-based recommendation systems.…”

Section: Review Of the Literaturementioning

confidence: 99%

User Evaluation-Driven Ranking Concept

Zosimov,

Bulgakova,

Perederyi

2023

RIC

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Context. The problem of personalizing search engine results, empowering users with search result management tools and developing new ranking models based on user’s subjective information needs. The object of the study was to modeling information search results in the Internet based on user ratings. Objective. The goal of the work is to form unique expert groups for each user, based on calculating the measure of agreement between the current user’s opinions and potential experts. Method. Introducing a novel method for ranking search results based on user ratings, which takes a subjective approach to the ranking process. This approach involves the formation of distinct expert groups tailored to individual users. Experts are selected based on the level of agreement between their opinions and the current user, determined by shared ratings on a specific set of web resources. User selection for the expert group is based on their weight relative to the current user, serving as a measure of agreement. The proposed methodology offers a fresh approach to forming unique expert groups for each user, utilizing three different strategies depending on the presence of shared ratings on a particular set of web resources between the user and potential experts. The developed ranking method ensures that each user receives a personalized list of web resources with a distinct order. This is accomplished by incorporating unique ratings from the expert group members associated with each user. Furthermore, each rating contributes to the ranking model of web resources with an individual weight, calculated based on an analysis of their past system activity. Results. The developed methods have been implemented in software and investigated for complex web data operation in real time. Conclusions. The conducted experiments have confirmed the effectiveness of the proposed software and recommend its practical use for solving complex web data operation in real time. Prospects for further research may include optimizing software implementations and conducting experimental investigations of the proposed methods on more complex practical tasks of various nature and dimensions

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Enhancing Readability in Custom Templates for Displaying Semantically Marked Information

Zosimov,

Bulgakova

2023

Lecture Notes on Data Engineering and Communications Technologies

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Complex Internet Data Management System

Cited by 5 publications

References 5 publications

Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

User Evaluation-Driven Ranking Concept

Enhancing Readability in Custom Templates for Displaying Semantically Marked Information

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